Digital breast tomosynthesis (DBT) is an emerging x-ray breast imaging modality that scans the breast from multiple angles, allowing reconstruction of the breast's interior into a pseudo-3D image. While optimization variables in mammography are limited to x-ray tube voltage and exposure, DBT offers additional optimization possibilities such as scan angular range. Previous studies have established that wide-angle DBT excels in detecting larger objects, such as tumors, while narrow-angle DBT is superior in detecting smaller structures such as microcalcifications. Therefore, it would be advantageous to choose an option between narrow- and wide-angle scans in a patient-specific manner. In this study, we propose a method that utilizes pre-exposure scan data obtained during the automatic exposure control (AEC) process immediately before actual DBT scanning to predict patient lesion information in advance. We generated standard dose mammography and DBT pre-exposure scan using Monte Carlo-based numerical simulation. We trained a U-Net with added WGAN loss using this pair. Using this model, we synthesized pseudo-pre-exposure images from a real mammography dataset. Subsequently, a YOLO-based classification network was employed to distinguish whether masses were present or absent in the corresponding pre-exposure images. The trained network demonstrated an accuracy of 0.87 and an AUROC of 0.95, which is comparable to those of a classifier network using conventional mammography. A paired t-test also suggests that there is no statistically significant difference between the classifiers (t = 0.22). This study may contribute to enhancing breast cancer detection performance by proposing a patient-specific DBT scan range option.
Digital breast tomosynthesis (DBT) provides pseudo-3D images by acquiring limited angle projections, thus alleviating an inherent limitation of tissue superposition in digital mammography (DM). DBT performance, however, may have limitations in terms of recovery of low-contrast structures and accuracy of material decomposition due to scatter radiation. Employing an anti-scatter grid in DBT can mitigate scatter radiation; however, this would lead to the loss of primary radiation. To compensate for the loss, an increased radiation dose is necessary. Additionally, it requires extra manufacturing costs and adds to the system’s complexity. In this work, we propose a deep-learning approach inspired by asymmetric scatter kernel superposition to estimate scatter in DBT. Unlike conventional kernel-based methods which estimate the scatter field based on the value of an individual pixel, the proposed method generates the scatter amplitude and width maps through a network. Additionally, the asymmetric factor map is also estimated from the network to accommodate local variations in conjunction with the object thickness and shape variation. Experiments demonstrate the superiority of the proposed approach. We believe the clinical impact of the proposed method is high since it can negate the additional radiation dose and the system complexity associated with integrating an anti-scatter grid in the DBT system.
2D synthetic radiography image can be computed from quasi-3D volume image produced by digital tomosynthesis (DTS) module negating additional radiation exposure for a separate 2D X-ray imaging. In our earlier work, we have developed a prototype DTS system that is equipped with an array of carbon-nanotube (CNT) X-ray sources. In this work, we develop an algorithm for synthesizing 2D image from the DTS-reconstructed volume image in the source array-based DTS system. Since the system uses a 2D array type source, the image artifacts due to the out-of-plane structures manifest relatively uniformly in all directions in the image slice unlike typical tomosynthesis systems. We have developed a smooth-manifold-extraction (SME) based method, which has been used in the field of confocal microscopy, for 2D image synthesis. Unlike microscopy, high-density structures exist at varying depths in a human body. Therefore, the SME algorithm was modified to apply to our DTS system.
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